In the imbalanced support set setting, we meta-train FSL methods using standard episodic meta-training [1]} using 5-shot 5-way tasks. We also explore random-shot episodic training [2]} using 1-9shot 5-way random-distribution tasks (as described in section ). We meta-/pre- trained on 100k tasks/mini-batches, using a learning rate of \(10^{-3}\)  for the first 50k episodes/mini-batches, and \(10^{-4}\)  for the second half. The baselines and SimpleShot are trained using 100k balanced mini-batches with a batch size of 128. All methods were meta-validated on 200 tasks/mini-batches every 500 meta-training tasks/mini-batches to select the best performing model.
